Associations between rainfall variability in the southwest and southeast of Australia and their evolution through time

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 30: (2010) Published online 18 June 2009 in Wiley InterScience ( DOI: /joc.1964 Associations between rainfall variability in the southwest and southeast of Australia and their evolution through time Pandora Hope, a * Bertrand Timbal a and Robert Fawcett b a Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, Australia b National Climate Centre, Bureau of Meteorology, Melbourne, Australia ABSTRACT: Significant rainfall declines have been observed across much of southern Australia over the last 50 years. In particular, strong declines occurred in the southwest of Western Australia (SWWA) from the late 1960s with further declines in the late 1990s, and in the southwest of eastern Australia (SWEA) from the mid-1990s. As a result, most communities in these regions are facing water shortages. Meteorologically, the regions share a common winter-maximum rainfall regime, with peak rainfall occurring from May to October. In both regions, the declines have mainly occurred in the early part of the winter half-year (May June July, MJJ). This study aims to assess if there is an association between rainfall variability in these two regions on a range of time-scales and whether that association has altered with the recent rainfall declines. Rainfall variability in the two regions is significantly related. On synoptic time-scales, a weak but significant relationship was found in each month of the winter half-year due to rain-bearing disturbances travelling along the storm track affecting the two regions with a lag of approximately 3 days. On interannual time-scales, the relationship is stronger because of the high correlation between mean sea-level pressures (MSLP) over both regions. MJJ MSLP has seen significant increases over the last 50 years in both regions, reaching record values in In recent decades the interannual association between early winter rainfall totals has strengthened, however this is due in part to the decreasing variance at each location. The variability of the associations between these two regions provides a background on which to explore the strength and variability of the drivers that influence rainfall variability across southern Australia. Copyright 2009 Royal Meteorological Society KEY WORDS Australia; rainfall; linkages; drivers; variability; southwest Australia; southeast Australia Received 30 October 2008; Revised 6 May 2009; Accepted 16 May Introduction The southwest corner of Western Australia (SWWA) and the western portion of southeastern Australia (SWEA) display similarities in their seasonal rainfall signature and geographical positioning relative to rain-bearing weather systems in the westerlies (Figure 1). Both regions have a winter dominated seasonal cycle (Figure 2) (Kane, 1997). During winter they are located poleward of the subtropical ridge (Peixoto and Oort, 1992; Figure 1). On the day-to-day time-scale, both regions are affected by rainbearing fronts travelling with the mid-latitude westerly circulation (Gentilli, 1972; Wright, 1974, Hope et al., 2006; Pook et al., 2006). It is thus likely that the same weather system travelling from the west across southern Australia might impact on rainfall variability in both regions. This study aims to assess if there is indeed a linkage between rainfall variability in these two regions on a range of time-scales. Rainfall totals across southern Australia have seen important reductions in recent decades (Figures 2 and 3) * Correspondence to: Pandora Hope, Bureau of Meteorology, GPO Box 1289, Melbourne 3001, Australia. p.hope@bom.gov.au (Alexander et al., 2007; Gallant et al., 2007; Taschetto and England, 2009; products/cli chg/). It is now well established that rainfall in the early part of winter decreased in the SWWA in the late 1960s (IOCI, 2002). The reduction was about 17% of the winter mean and rainfall has not recovered since, with arguably a further decline since the mid-1990s (Ryan and Hope, 2005, 2006). This has had major impacts on water supplies for Perth (Environmental Protection Authority, 2007), and varying impacts on agriculture (e.g. Fulco et al., 2009). Rainfall variability in the southeast particularly impacts upon agricultural production (Nicholls, 1997) and ecosystem survival (e.g. Murray-Darling Division of the Department of Water, Land and Biodiversity Conservation, 2003). The southeastern part of Australia has experienced a protracted drought since 1996 (Bureau of Meteorology, 2006). Like SWWA, it is particularly in autumn and early winter (Murphy and Timbal, 2008) when the major reductions have occurred. The longer-term rainfall variability in these regions was not aligned through the second half of the 20th century; however, the possible declines in the 1990s in both regions might represent a growing association Copyright 2009 Royal Meteorological Society

2 RAINFALL LINKS ACROSS SOUTHERN AUSTRALIA 1361 Figure 1. The long-term 1850 to 2004 mean of May to October MSLP from the HadSLP2 dataset around the Australian continent. The shading corresponds to the SWWA and SWEA regions over which the rainfall is averaged. The boxes overlying these regions are the extent of the MSLP average corresponding to SWWA and SWEA. This figure is available in colour online at (a) mm Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month (b) mm Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 2. Mean monthly rainfall in (a) SWWA and (b) SWEA for different epochs. between the two regions. Ansell et al. (2000) confirms that the two regions have at best a weak association on interdecadal time-scales. They found that June August rainfall in SWWA and a district within SWEA over the period had spectral peaks on longer timescales. A peak at about 9 years is nearly in phase between

3 1362 P. HOPE ET AL mm SWWA MJJ rainfall SWEA MJJ rainfall Figure 3. May to July (MJJ) SWWA and SWEA mean rainfall (mm). Downward arrows point to the break-points in the SWWA time series, while the upward arrow points to the most likely point for a break in the SWEA time series. Year the two regions, but not quite. However, more recent data since the mid-1990s, when the greatest declines were observed in SWEA, were not included in their analysis and interdecadal associations may now be stronger. The period since 1995 is not long enough to explore whether associations on interdecadal timescales are strengthening, thus the present work primarily examines associations on daily and interannual time-scales. If links are found between rainfall variability in SWWA and SWEA, this might be cause for heightened concern that the recent SWEA declines might persist for decades as did the decline in SWWA since the 1970s. Major large-scale indicators of climate variability and drivers of the Southern Hemisphere climate influence rainfall variability in these regions in the same manner. Mean sea-level pressure (MSLP) has been demonstrated to be closely tied to rainfall in these regions (e.g. Allan and Haylock, 1993; Smith et al., 2000; Timbal and Murphy, 2007). It has been effectively used as an important predictor of localised rainfall statistics in SWWA (Charles et al., 1999; Timbal, 2004) and SWEA (Timbal and Jones, 2008). In winter and autumn both Meneghini et al. (2007) and Hendon et al. (2007) found that higher (lower) values of the Southern Annular Mode (SAM) are related to lower (higher) values of rainfall, except along a small section of the southern coast of SWWA, where the relationship is reversed. The majority of the area defined as SWWA (Figure 1) responds in the same direction as SWEA. The direction of the relationship with SAM also changes sign further north of SWEA (Timbal and Murphy, 2007). The relationship between SAM and southern Australia rainfall weakens later in the winter half-year (Williams and Stone, 2008). Both SWWA and SWEA are also drier than average during an El Niño and wetter than average during a La Niña (McBride and Nicholls, 1983; shtml). Although both regions display a relationship with these large-scale drivers, the relationship in SWWA is weak. SAM plays a stronger role than ENSO on rainfall variability in both regions in winter, while in spring ENSO plays a significant role in SWEA (McBride and Nicholls, 1983; Meneghini et al., 2007). Variability in the Indian Ocean is also associated with southern Australian rainfall variability (e.g. Smith et al., 2000; Ashok et al., 2003; Ummenhofer et al., 2008). The links to these largescale drivers vary through time also (e.g. Wright, 1988) as does the variability of the drivers themselves (Karoly et al., 1996; Power and Smith, 2007; Nicholls, 2008). The large-scale forcing behind the 1970s rainfall decline in SWWA has been explored extensively (as described in Ryan and Hope, 2005, 2006). If this study reveals a link between rainfall variability in SWWA and SWEA, these forcings should be considered when attempting to understand the recent SWEA rainfall decline. The overall conclusion reached from climate model studies was that natural variability alone can produce similar downturns in rainfall (Cai et al., 2005) to those observed in SWWA, however, under the actual forcing over the last 50 years, the downturn was not simulated unless greenhouse gas forcing was included (Ryan and Hope, 2005; Timbal et al., 2006). These broad-scale changes partly manifest through the region s response to SAM (Cai and Cowan, 2006); as mentioned earlier SAM also influences SWEA, thus this might be a mechanism by which the association between the two regions is strengthened. Local land-cover changes were also a plausible contributor to the SWWA rainfall decline in the 1970s (Pitman et al., 2004; Timbal and Arblaster, 2006). However, preliminary work has not identified land-cover change as a contributor to recent rainfall declines in SWEA (Narisma and Pitman, 2003). After introducing the datasets used (Section 2), we describe in detail the long-term observed rainfall shifts that serve as part of the motivation for this study (Section

4 RAINFALL LINKS ACROSS SOUTHERN AUSTRALIA ). The relationship between rainfall in the southwest and southeast of Australia on interannual time-scales is then explored in Section 4. In Section 5, we examine the strength of that relationship on daily scales and the role of synoptic disturbances travelling along the southern coast of Australia and affecting both corners of the continent. Finally, the elements which describe the similarities between these two regions and how they vary through time are summarised in Section Data 2.1. Rainfall The monthly rainfall dataset is the Australian Bureau of Meteorology s National Climate Centre (NCC) gridded monthly rainfall analyses from 1890 to The data is on a 0.25 by 0.25 latitude longitude grid, based on all station data of reasonable quality available at any one time (Jones and Weymouth, 1997). Temporal variations in the network can therefore have an impact on the trends. Analogous daily gridded analyses from 1900 to 2007 were also obtained from the NCC (Mills et al., 1997). Daily and monthly time series of area-averaged SWWA and SWEA rainfall were created. The reasons as to why this study focusses particularly on the rainfall variability from the small regions in Figure 1 are outlined here. The regions were chosen to incorporate much of the region where the annual average rainfall (Bureau of Meteorology, 2000) is greater than 500 mm. These areas have also seen significant rainfall declines in recent decades; maps of rainfall means and trends are available from chg/. The area affected by the rainfall decline in the southeast is large compared to that of SWWA. The decline is particularly strong in the SWEA comprising the western part of Victoria and the southern part of South Australia (Bureau of Meteorology, 2006). The rainfall in the SWEA region is also more closely associated with pressure anomalies and onshore westerly winds (Whetton, 1988) compared to other parts of southeastern Australia. It is thus this region that is most likely to be associated with rainfall variability in the west. The SWEA region is limited to the area to the west of the Great Dividing Range. The SWWA region is referred to in studies by the Indian Ocean Climate Initiative (e.g. IOCI, 2002). Clustering of seasonal rainfall data by Drosdowsky (1993) revealed SWWA as clearly distinct from its surrounding regions in all seasons. Using station data, Nicholls and Lavery (1992) found a subregion that Drosdowsky (1993) could not capture due to his use of district rainfall data. This subregion also corresponds to the region where the relationship between the SAM and winter rainfall is reversed compared with the rest of SWWA and SWEA (Hendon et al., 2007; Meneghini et al., 2007). The SWWA triangle does not extend far into this region Mean sea-level pressure MSLP is used in this study to help analyse the links between our two regions of interest. MSLP is a useful measure since it is known to be related to rainfall variability, and it does not suffer from the strong spatial heterogeneity that rainfall can display, nor from the zero bound. For instance, a rainfall system does not necessarily drop the same amount of rainfall over SWWA and SWEA, however, the pressure is likely to fall by a similar amount as the system traverses southern Australia. A global MSLP dataset (HadSLP2) from 1850 to 2004 on a 5 by 5 grid (Allan and Ansell, 2006) was used throughout this analysis. The MSLP was averaged over two boxes: 27.5 to 37.5 S and to E for SWWA and 32.5 to 42.5 S and to E for SWEA (see Figure 1). Timbal and Hope (2008) compare HadSLP2 with reanalyses over these regions for the period and, although it has lower interannual variability, it is believed to be of high enough quality to use for our purposes. 3. Observed long-term shifts in winter rainfall Murphy and Timbal (2008) and Bureau of Meteorology (2006) describe the downturn in rainfall in southeast Australia since 1996 and Ryan and Hope (2005, 2006) suggest that there has been a further decline in rainfall in SWWA in the late 1990s. Part of the motivation of this study is the decline in rainfall in the 1990s in SWWA and SWEA, thus in this section we examine the long-term rainfall time series from the particular regions chosen in this study to identify the significance of these declines. We use the most recent data and apply a break-point method that can identify breaks closer to the end of the series than the one used in IOCI (1999) and Ryan and Hope (2006). We place particular emphasis on the changes in early winter, taken here to signify the months May to July (MJJ). From a purely statistical perspective, the timing of distinct shifts in the mean of a given time series can be revealed using break-point analysis. The shift in the mean of SWWA MJJ rainfall in the late 1960s is very robust. Here, we include the most recent data and retest for break-points in the MJJ time series from both regions. Three methods were used. These include the nonparametric Mann Whitney Pettitt method (Pettitt, 1979) for detecting a single break-point within a time series, its analogous parametric method based on the assumption of a normal distribution (Pettitt, 1979), and a non-parametric two-break-point implementation involving the replacement of the Mann Whitney test within the technique by the Kruskal Wallis test (e.g. Freund, 1988). Statistical significances were estimated by Monte-Carlo resampling of the time series with replacement in the parametric method and without replacement in the non-parametric methods. This empirical approach to the estimation of significance takes into account the fact that the breakpoint method involves searching along the entire time

5 1364 P. HOPE ET AL. Table I. Mean climatological May to July and August to October rainfall (in mm) for different periods for both SWWA and SWEA and associated rainfall changes calculated as a percentage of difference between two periods divided by the mean. As noted in the text, 1968/1969 is a statistically significant break for the SWWA MJJ time series (95% two-tailed). SWWA MJJ SWEA MJJ SWWA ASO SWEA ASO mean mean mean mean mean mean mean mean minus as a % of mean 17% 4% 8% 3% mean minus mean as a % of mean 13% 0% 10% 8% mean minus mean as a % of mean 21% 15% 0% 10% series for the most probable break-point, thereby invalidating those estimates of significance which rely on the assumption of a fixed (or pre-specified) break-point. In SWEA, the most likely break in the full MJJ rainfall record from 1890 to 2007 is a decline at 1996/1997. This break is not statistically significant, although the percentage change is large (Table I). In the corresponding SWWA MJJ time series, all three techniques yielded a statistically significant decline (95% two-tailed) at 1968/1969 consistent with the previously reported results mentioned above. Testing for two breaks reveals an upward step at 1903/1904, together with the downward step at 1968/1969. The decade from 1890 to 1900 comprises near-average rainfall totals of low interannual variability, and, on shortening the time series by removing this first decade and retesting for a second break, a further significant reduction in the mean of the SWWA MJJ rainfall is evident across 1999/2000. This combination of the 1968/1969 and 1999/2000 breaks is stable with respect to changes in the start year of the calculation. The years highlighted from the breakpoint analysis are used to construct averages over each period (1997 is used as the first of the final period to be consistent across SWWA and SWEA) and these are presented in Table I and Figure 2. Using this particular data set and definition of each region, SWEA has seen a reduction of 15% from the long-term mean in the last 11 years, while SWWA has seen an even greater reduction of 21%. The August September October (ASO) rainfall time series in both regions shows no significant break in the last 50 years, although in SWWA there is a decline in 1935/1936 that is almost significant (i.e. significant at the 90% two-tailed level, but not at the 95% level). Table I also shows that there was a significant decline in mean rainfall over the period compared to the long-term mean, however this reduction has not continued. The direction of these changes found using breakpoint methods is likely to be meaningful and significant, however the timing of these breaks may be influenced by internal variability. It should be noted that the break-point detection approach is in effect looking for statistically significant departures from the null hypothesis of no change, and the detection of a statistically significant departure does not guarantee that departure is of precisely the step change nature presupposed by the statistical methodology. 4. Variability on interannual time-scales There is significant interannual correlation between MJJ rainfall in SWWA and SWEA as seen in Figure 4, where only significant correlations are shown. This supports the suggestion by Whetton (1988) that rainfall in the west of southeast Australia is more highly associated with storms embedded in the westerlies compared to the rest of the southeast. The correlation between the spatially averaged MJJ rainfall over the two triangles shown in Figure 1 for the years is r = 0.41, significant at the 99% level. The autocorrelation in each time series is low. The scatter plot of the two MJJ rainfall time series is shown in Figure 5(a). SWEA has a lower rainfall total (198 mm) than SWWA (304 mm), thus if a linear association between seasonal rainfall in the two regions is assumed, a line of proportionality through the cloud in Figure 5(a) would have a slope of approximately 2 : 3. There are a number of years that stand apart from the cloud such as 2006, which was particularly dry in both locations, and 1923, which was particularly wet in both regions. There are also a number of years with particularly low values of rainfall in SWEA compared to what would be expected from the value in SWWA, and these fall well away from the line of proportionality. Although an examination of the drivers behind the associations found between our two regions of interest is beyond the scope of this paper, a number of years stand out as falling on the line of proportionality while others fall well away from it, and those years are associated with particular drivers. A number of years influenced by ENSO fall clearly on the line of proportionality, for example 1956 and 1988 were La Niña years and lie

6 RAINFALL LINKS ACROSS SOUTHERN AUSTRALIA 1365 Figure 4. Correlation of the interannual variability between the MJJ SWWA rainfall time series and rainfall at every grid-point across Australia. Only correlations significant at the 95% level or greater are shown. on the upper end line in Figure 5(a). Years with lower than expected values in SWEA, compared to the totals in SWWA (e.g. 1945, 1946, 1967) do not coincide with strong El Niño or La Niña events, they do however coincide with positive Indian Ocean Dipole (IOD) events (Meyers et al., 2007). The corresponding scatter diagram for ASO rainfall is shown in Figure 5(b). Average total ASO rainfall in SWEA is closer to that in SWWA, thus the line of proportionality has a slope close to 1 : 1. The correlation between SWEA and SWWA in this season is lower than MJJ: r = 0.29, which could be gathered from the wider scatter in Figure 5(b). Although the scatter is wide, there appears to be a tail of years with particularly wet conditions in SWWA, but only slightly wetter than average conditions in SWEA. These years (1890, 1903, 1917 and 1955) are associated with La Niña events (Meyers et al., 2007). Years with very wet conditions in SWEA and below average rainfall in SWWA correspond with a mix of events as defined by Meyers et al. (2007). Thus although these scatter plots have revealed a number of interesting associations, the drivers are not always clear, and a full analysis of the drivers would require a much closer examination of the particular signature and seasonal timing of each event, along with other drivers such as SAM or the intensity of the Hadley cell. The idea that cool sea surface temperature anomalies off the north west of Australia (positive IOD) might lead to reduced rainfall in SWEA compared to what might be expected from the SWWA total was briefly explored further. This result might be expected as the cool sea surface temperatures might feasibly limit the frequency of northwest cloud-bands that are believed to be linked with an increased potential for positive anomalies in SWEA rainfall (e.g. Wright, 1997). The Meyers et al. (2007) IOD index time series was extended (McIntosh, pers. comm., 2008) to include more recent years and allow a seasonal breakdown. It was found that 2006 had a positive IOD index in late winter conditions were the driest on record in SWWA and SWEA in MJJ, and close to the line of proportionality. However, in late winter (Figure 5(b)), when the positive IOD became established, 2006 was the driest on record in SWEA, but only slightly drier than average in SWWA. Two further years with very low SWEA totals, but average to aboveaverage rainfall in SWWA in ASO (Figure 5(b)) were also positive IOD years (2007 and 1977). The impacts in 2007 (e.g., Hope and Watkins, 2008) were despite 2007 being an unusual event that occurred during a La Niña (Cai et al., 2009). Thus, not all years with a positive IOD lead to dry conditions in SWEA throughout the winter half-year, but many of the seasons with unusually dry conditions in SWEA compared to their counterpart in SWWA are associated with positive IOD events. As suggested by the scatter in the cloud shown in Figure 5(a), the strength of the interannual association between MJJ rainfall in SWWA and SWEA varies through time. To display this clearly, Figure 6 shows the interannual correlation (and covariance) in 21-year time-slices. In Figure 3 the time series of MJJ rainfall in SWWA and SWEA are often in phase. The correlation over the full period is r = The correlations weaken in the 1930s and 1940s and again briefly in the 1970s. Since then the correlations have been consistently high, the correlation between MJJ rainfall in SWWA and SWEA over the period 1968 to 2007 is r = The rise in correlations since the late 1960s is due in part to a decrease in interannual rainfall variability over

7 1366 P. HOPE ET AL (a) SWWA vs SWEA rainfall - MJJ SWEA rainfall in mm AVERAGE SWWA rainfall in mm (b) SWWA vs SWEA rainfall - ASO SWEA rainfall in mm AVERAGE SWWA rainfall in mm Figure 5. Scatter plot of SWWA and SWEA rainfall for (a) MJJ and (b) ASO. Years are marked on the plot. The line of proportionality based on the long-term average is shown as a thick grey line. This figure is available in colour online at this time; the covariance over this recent period is only slightly higher than the long-term mean covariance (Figure 6). The statistical significance of these moving 21-year correlations and covariances has been assessed by means of Monte-Carlo simulations ( iterations sampling with replacement from the full time series and assuming negligible autocorrelation in both time series). The 21-year correlations in Figure 6 stay entirely within the computed 90% confidence interval (+0.02 to +0.71), with the 21-year covariances straying briefly outside the corresponding computed 90% confidence interval for 21- year periods centred around It has long been recognised that rainfall anomalies in mid-latitudes are closely related to MSLP anomalies. The links between rainfall and MSLP in the datasets used in this study were assessed. MJJ Rainfall and MSLP are highly negatively correlated (r = 0.67 for SWWA and 0.77 for SWEA), significant at the 99% level. MJJ is the time of year with the highest correlation. A simple path analysis (Figure 7) reveals that all the rainfall relationship between SWWA and SWEA can be accounted for by the spatial relationship between MSLP at both ends of the continent and the relationship between MSLP and rainfall in each location. The MSLP time series averaged over the two boxes are highly correlated in MJJ (r = 0.80). A scatter plot of SWWA and SWEA MJJ MSLP sits tightly on the line of proportionality between the two means (Figure 8). Years with a particularly strong MJJ rainfall signature in both SWWA and SWEA are expressed in the MSLP: 2006 lies

8 RAINFALL LINKS ACROSS SOUTHERN AUSTRALIA correlation covariance Correlation (r) Covariance Year Figure 6. Correlation and covariance of SWWA and SWEA MJJ total rainfall in 21-year time-slices. The value is placed at the mid-year of the 21-year period. Figure 7. Path analysis of the relationship between rainfall in SWWA and SWEA in MJJ through to the relationship between MSLP over each region. SWEA MSLP (hpa) SWWA vs SWEA HADSLP - MJJ SWWA MSLP (hpa) Figure 8. Scatter plot of MJJ HadSLP MSLP averaged over squares surrounding SWWA and SWEA (see Figure 1) for the years The line of proportionality based on the long-term average is also shown. The point in the top right is This figure is available in colour online at far above the cloud, and the lowest point corresponds to 1923, a very wet year (Figure 5(a)). Years where MJJ rainfall was high in SWWA, but low in SWEA, such as 1967, fall within the cloud, suggesting that the relationship between MSLP and rainfall in SWEA might be less strong during these years and MJJ rainfall totals were influenced by factors other than local MSLP. The correlation between MJJ MSLP over SWWA and SWEA does not vary greatly through time, correlations in 21- year time-slices remain close to r = 0.8. They briefly fell below r = 0.6 at times in the 1930s and 1950s (not shown). Through time, broad-scale MSLP has been increasing across southern Australia (e.g. IOCI (2002) show the change across 1975), thus the rainfall decreases across southern Australia have occurred on a background of rising MSLP. Figure 9 shows the time series of HadSLP MSLP averaged over the areas shown in Figure 1 over SWWA and SWEA. The upward trend over this period is clear. Again, 2006 stands out as a value far beyond anything experienced before. Further analysis of the trends in the southern Australian region and SWWA and SWEA can be found in Timbal and Hope (2008). The factors driving this pressure increase are beyond the scope of this study, but there have been many relevant studies that might be explored (Lu et al., 2007; Gastineau et al., 2008). 5. Variability on daily time-scales It has long been recognised that as weather systems progress along the storm track from west to east, it is likely that the same broad trough will impact both SWWA and SWEA (Gentilli 1972; Wright, 1974). It would thus be expected that associated daily rainfall would exhibit a lag-correlation. The timing of this lag would depend upon how quickly the system travels along 2300 km separating the western edges of SWWA and SWEA. To test the link between daily rainfall in SWWA and SWEA, lagged (0 5 days) correlations were calculated for each month between 3-day sliding mean rainfall (a 3-day average was chosen to account for the duration of individual weather events) over the years , using the area-averaged daily rainfall time series for each region. To calculate the significance of the correlations, the autocorrelation is accounted for following the method described in Power et al. (1998). The

9 1368 P. HOPE ET AL SWWA MSLP SWEA MSLP hpa Year Figure 9. Average MJJ HadSLP MSLP time series for regions over SWWA and SWEA Correlation * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Lag Apr May Jun Jul Aug Sep Oct Nov Figure 10. Correlation between SWWA and SWEA 3-day averaged rainfall at various lags in days (from 0 to 4 days) for selected months of the year based on daily rainfall from 1948 to Asterisks denote statistically significant correlations. correlations are small at all lags; however, there are many statistically significant values. At 2 and 3 days lag, correlations are significant from April to November (Figure 10). From April to August, correlations are still significant with a 4-day lag. Correlations drop away markedly at long lags. At zero lag, the significant negative correlation between 3-day average rainfall totals in SWWA and SWEA for the months May to October indicate that when a system is raining in one location it is not in the other. The correlations then become positive and significant at 2, 3 and 4 days lag. The averaging of the data over 3 days and the variable speed of systems crossing southern Australia might account for this range in lags with significant correlations. Thus some of the rain falling in SWEA is correlated with rainfall that fell in SWWA a few days before, presumably associated with the same surface weather system travelling at about m/s. The highest correlations between SWWA and SWEA winter rainfall on a daily time-scale are significant, but small. Thus there is the potential that there is high interannual variability in the strength of the association. The correlation between SWWA daily rainfall and SWEA rainfall 3 days later was calculated for all days throughout MJJ for each year (denoted r R,lag ) from 1900 to Correlations are generally positive, but drop to near zero in the late 1960s for a number of years, and also in the 1910s, 1940s, early 1990s and 2002 (Figure 11). These periods with very low correlations might point to times when there is a disconnection between the amounts of rainfall falling from travelling systems over SWWA compared to SWEA.

10 RAINFALL LINKS ACROSS SOUTHERN AUSTRALIA r Rlag r (r Rlag, SWWA rain) r (r Rlag, SWEA rain) 0.40 r Year Figure 11. The correlation between daily rainfall in SWWA and rainfall in SWEA 3 days later throughout May July (r R,lag ) for each year (dashed curve). The correlation between r R,lag and total MJJ rainfall in SWWA (thick black line) or SWEA (thick grey line) in 21-year time-slices. At the daily time-scale, this relationship explains less than 10% of the rainfall variability, representing a small percentage of the very large daily variability of local rainfall. However, this relationship is significant on climatological time-scales as it offers a simple mechanism to link rainfall in these two areas. The strength of the association between the two regions on a daily time-scale (r R,lag ) has been positively associated with total MJJ rainfall in recent years. In Figure 11, years with high values of r R,lag over the last decade (1995, 1999 and 2004) align with upward spikes (relatively wet conditions) in SWWA in each year and wet conditions in SWEA in 1995 and 2004 (Figure 3). Recent years with low values of r R,lag (2002 and 2006) correspond to low values of total MJJ rainfall in both regions. To explore whether this pattern is important throughout the record, correlations in 21-year time-slices are shown between r R,lag and total MJJ rainfall in SWWA or SWEA in Figure 11. The pattern of association is high in recent years for both SWWA and SWEA; however, it was previously low from about 1940 to Early in the record associations were very high, and this period would be worthy of further analysis with high-quality station data. The rise in recent years is still evident when the lower interannual variability in the SWWA and SWEA total rainfall time series are accounted for. In a similar manner to that of Figure 6, the departures of these moving 21-year correlations in Figure 11 from their whole-period correlations was assessed by means of a Monte-Carlo simulation. The SWEA moving 21-year correlations (grey line) stay entirely within the computed 90% confidence interval ( 0.12 to +0.54), with the corresponding SWWA correlation (black line) straying only briefly outside its 90% confidence interval ( 0.18 to +0.56) around Positive correlations between r R,lag and total MJJ rainfall in SWWA might be expected if it assumed that a strong frontal system that brings a great deal of rainfall to SWWA will persist and also bring rainfall to SWEA. There is a positive correlation between r R,lag and the number of deep low pressure systems (as identified by Hope et al. (2006)) crossing the SWWA region (r = 0.33) since The positive association between r R,lag and MJJ total rainfall in SWEA over the last decade is very likely to be associated with the corresponding jump in the importance of frontal systems to total rainfall across a broad region within SWEA as compared to cut-off lows, which are the usual driver of interannual variability in the region (Pook et al., 2006; 2009 (in press)). The atmospheric conditions associated with periods when frontal systems dominate the rainfall in the region are conducive to the same frontal system impacting SWWA and SWEA (Risbey et al., 2009 (in press)). 6. Summary and conclusions The relationship between rainfall variability in the SWWA and the SWEA has been evaluated. There are significant associations on daily and interannual time-scales during the winter months. One major caveat on this finding is that this analysis was done using averaged gridded data, thus temporal variations in the spatial coverage of the station network from which these grids were produced might influence these results. Some of the associations vary through time. In recent years the association between MJJ total rainfall in SWEA and SWWA has been seemingly stronger than at many times in the past; however, a major component of this strengthening link is the decreasing variability within each of the time series as the rainfall totals decline. On the daily time-scale, the lagged association with rainfall throughout MJJ in SWWA compared to SWEA 3 days later has seen a growing positive association with total MJJ rainfall. The growing impact of frontal systems

11 1370 P. HOPE ET AL. compared to cut-off lows on rainfall in SWEA is a likely contributor to this finding. The interannual association between rainfall in these two regions is driven largely by the link between local rainfall and MSLP, however, other factors also contribute in years where rainfall totals between the two regions differ significantly from what might be expected if a linear relationship between the respective means in the two regions is assumed. There are a number of years that suggest that ENSO strengthens the seasonal link between the two regions and positive IOD events weaken it. These guidelines apply to some years that stand out in the rainfall datasets, but do not necessarily always guide the expected rainfall response in SWWA and SWEA. The signature, seasonality and full extent of large-scale climate phenomena associated with rainfall variability in these regions (e.g. SAM and the intensity of the Hadley cell as well as ENSO and other oceanic conditions) would need to be fully explored before definitive statements could be made about the drivers that strengthen and weaken the relationship between these regions. The growing MSLP across southern Australia, and the strong association between local MSLP and rainfall, suggests that the background signal driving weather systems in each region is becoming very strong, with 2006 being an extreme example. If this trend was to continue, this might be reason for concern in SWEA that the current 11-year drought may well continue into the future. SWWA might also see a continuing drying signal. Both regions have similarities in their exposure to frontal systems in the westerlies, winter dominated seasonal cycles and strong association with local MSLP, thus there was the expectation that there might be an association between rainfall on daily time-scales and rainfall and MSLP on interannual time-scales. We indeed found significant associations. However, there are also key differences between the two regions. These are primarily associated with their location on the Australian continent. SWWA has ocean and a climatological trough to its west, while SWEA has land to its northwest, the Great Dividing Range and then the Tasman Sea to the east and a climatological ridge to its north. Thus each region can be affected by more localised systems. The availability or lack of moisture from tropical sources will also add another element of variability to the time series. Thus the fact that the association between rainfall in both regions has gone through periods where it is relatively weak might also have been expected. The variability of these associations through time suggests that the strength of the drivers of rainfall in each region has also varied through time and this type of analysis can be used as a guide to better understanding those drivers and their variability and level of influence. Acknowledgements The authors are indebted to T. Ansell (UKMO) who provided the HadSLP2 and helped in interpreting some results. The authors would like to thank the National Climate Centre for its work developing and maintaining the gridded rainfall dataset used in this study. The authors would also like to thank Monica Long, Barry Hanstrum, John McBride, Julie Arblaster, Harry Hendon and Ian Smith for their considered advice on aspects of this study. B. Timbal is supported by the South Eastern Australian Climate Initiative (SEACI). P. 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